Floating search methods in feature selection
Pattern Recognition Letters
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemblator: An ensemble of classifiers for reliable classification of biological data
Pattern Recognition Letters
Ensemble methods for classification of patients for personalized medicine with high-dimensional data
Artificial Intelligence in Medicine
Random subspace for an improved BioHashing for face authentication
Pattern Recognition Letters
Wavelet decomposition tree selection for palm and face authentication
Pattern Recognition Letters
Logistic Ensembles for Random Spherical Linear Oracles
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Combining SVM classifiers using genetic fuzzy systems based on AUC for gene expression data analysis
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Orthogonal linear discriminant analysis and feature selection for micro-array data classification
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The microarrays report the measures of the expression levels of tens of thousands of genes, this high dimensional feature vector contains also irrelevant information for accurate classification. Moreover, only few training samples are available, hence for avoiding the curse of dimensionality problem a feature reduction should be performed before the classification step. Here, we proposed a set of orthogonal wavelet detail coefficients of different wavelet mothers to extract the features from the microarray data. We propose to use a multi-classifiers where each classifier, a support vector machine, is trained using a different set of detail coefficients, the classifiers are combined by ''sum rule''. The detail coefficients set selection is performed by running Sequential Forward Floating Selection (SFFS). The goodness of the proposed method is validated using the area under the ROC curve as performance indicator, the experiments are carried out on four-datasets: Breast dataset; Ovarian dataset; Lung dataset; Prostate dataset. The results show that the proposed method outperforms the performance that can be obtained by a single set of detail coefficients. Moreover, we have shown that, also using as features the detail coefficients, a random subspace of classifiers outperforms the stand-alone classifiers.